Comorbidity, socioeconomic status and multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Multiple sclerosis (MS) is associated with substantial morbidity. The impact of comorbidity on MS is unknown, but comorbidity may explain some of the unpredictable progression. Comorbidity is common in the general population, and is associated with adverse health outcomes. To begin understanding the impact of comorbidity on MS, we need to know the breadth, type, and frequencies of comorbidities among MS patients. Using the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry, we aimed to describe comorbidities and their demographic predictors in MS. METHODS: In October 2006, we queried NARCOMS participants regarding physical comorbidities. Of 16,141 participants meeting the inclusion criteria, 8983 (55.7%) responded. RESULTS: Comorbidity was relatively common; if we considered conditions which are very likely to be accurately self-reported, then 3280 (36.7%) reported at least one physical comorbidity. The most frequently reported comorbidities were hypercholesterolemia (37%), hypertension (30%), and arthritis (16%). Associated with the risk of comorbidity were being male [females vs. males, odds ratio (OR) 0.77; 0.69-0.87]; age (age >60 years vs. age < or = 44 years, OR 5.91; 4.95-7.06); race (African Americans vs. Whites, OR 1.46; 1.06-2.03); and socioeconomic status (Income <$15,000 vs. Income >$100,000, OR 1.37; 1.10-1.70). CONCLUSIONS: Comorbidity is common in MS and similarly associated with socioeconomic status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".